The present disclosure relates, generally, to systems and method for processing optical images. More particularly, the disclosure relates to automatic detection of pulmonary embolisms using images.
Pulmonary embolisms (“PEs”) are blood clots that travels from the legs, or other parts of the body, to the lungs. In the lungs, blood clots can block central, lobar, segmental, or sub-segmental pulmonary arteries, depending on its size. If left undiagnosed, PEs lead to a mortality rate of up to 30%. However, with early diagnosis and treatment, the mortality rate can be reduced to less than 11%.
The primary imaging technique utilized for PE diagnosis is computed tomography pulmonary angiogram (“CTPA”). In CTPA, an embolus appears as a dark region surrounded by the brighter vessel lumen. Interpreting a CTPA dataset demands a radiologist to carefully trace each branch of the pulmonary vasculature for any suspected PEs. However, with a large number of arteries to be tracked and complexity of the images, PE diagnosis often requires extensive reading time whose accuracy depends on a clinician's experience, attention span, eye fatigue, as well as sensitivity to the visual characteristics of different PEs.
Computer-aided detection (“CAD”) can play a major role in helping clinicians diagnose PEs. In particular, recent clinical studies have shown that CAD systems can increase sensitivity to identifying PEs. However, despite a demonstrated utility, existing CAD technologies still produce relatively high false positives in order to achieve clinically acceptable PE sensitivity. Such high number of false positives, generated by CAD systems, prolong the reading time of CTPA studies because each PE candidate must be examined by a radiologist.
Therefore, in light of the above, there is a clear need for improved systems and methods for detecting pulmonary embolisms that can achieve higher sensitivity at a clinically acceptable false positive range.